[1]韩芝星,蔡晓龙.基于小波变换的提升机闸瓦间隙故障信号诊断[J].工业仪表与自动化装置,2021,(03):115-118.[doi:10.19950/j.cnki.cn61-1121/th.2021.03.024]
　HAN Zhixing,CAI Xiaolong.Hoist brake shoe signal gap fault diagnosis based on wavelet[J].Industrial Instrumentation & Automation,2021,(03):115-118.[doi:10.19950/j.cnki.cn61-1121/th.2021.03.024]
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基于小波变换的提升机闸瓦间隙故障信号诊断

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2021年03期
页码:
115-118
栏目:
出版日期:
2021-06-15

文章信息/Info

Title:
Hoist brake shoe signal gap fault diagnosis based on wavelet
作者:
韩芝星1; 蔡晓龙2
1.山西职业技术学院 电气自动化工程系，山西 太原 030006;
2.国网山西省电力公司检修公司，山西 太原 030032
Author(s):
HAN Zhixing1; CAI Xiaolong2
1.Shanxi vocational and Technical College，Electrical and Automation Engineering Department，Shanxi Taiyuan 030006,China；
2. National Grid Maintenance branch of Shanxi province electric power company, Shanxi Taiyuan 030032,China
关键词:
小波分析; 故障诊断; 小波变换
Keywords:
wavelet analysis;  fault diagnosis;  wavelet transform
分类号:
TD63+3
DOI:
10.19950/j.cnki.cn61-1121/th.2021.03.024
文献标志码:
A
摘要:
为了提高闸瓦磨损情况、闸瓦间隙变化的故障识别和诊断效率，从而使设备状态满足煤矿安全规程的各项具体要求。该文选用Daubechies5小波作为小波基，对提升机闸瓦间隙信号进行多分辨率分析，将突变信号进行多尺度分解，通过分解后的信号来确定突变信号的位置，通过分解后的3层高频重构图形可以具体清楚地确定突变信号的位置，实现提升机闸瓦间隙故障的诊断，对故障信号的位置进行较好地判断。
Abstract:
In order to improve the efficiency of fault identification and diagnosis of wear degree and clearance change of brake shoe, so as to meet the requirements of coal mine safety regulations. In this paper, daubechies5 wavelet is selected as the wavelet base to analyze the brake shoe clearance signal of hoist in multi-resolution, decompose the sudden change signal in multi-scale, determine the position of the sudden change signal through the decomposed signal, and clearly determine the position of the sudden change signal through the decomposed three-layer high-frequency reconstruction graph, so as to realize the fault diagnosis of the brake shoe clearance of hoist and the position of the fault signal Set to make a better judgment.

参考文献/References:

[1]孔国杰,张培林,徐龙堂,等.信号奇异性检测中的小波基选择及其工程应用[J].机械科学与技术,2009,28(04):542-545+552.

[2]李炎,高山.小波奇异性检测原理在风电数据处理中的应用[J].电网与清洁能源,2010,26(06):67-71+80.
[3]张德丰.基于小波的信号突变点检测算法研究[J].计算机工程与科学,2007(12):98-100.
[4]MALLAT S , HWANG W L . Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory, 2002, 38(2):617-643.
[5]张德丰. MATLAB 小波分析[M].机械工业出版社,2009.
[6] 郝晓娴,牛昱光,韩芝星,等.基于小波和BP神经网络的车载动态称重算法[J].仪表技术与传感器,2017(08):110-113+121.
[7]姚德臣,杨建伟,程晓卿,等.基于多尺度本征模态排列熵和SA-SVM的轴承故障诊断研究[J].机械工程学报,2018,54(09):168-176.
[8] 唐俊.浅析小波分析在矿山物探数据处理中的应用[J].世界有色金属,2019(14):215+218.
[9] 韩芝星,李丽宏,蔡晓龙,等.基于弯板传感器的动态称重公路预检系统研究[J].传感器与微系统,2015,34(08):64-66+82.?/div>
[10] 杨琴,彭力.基于量子小波神经网络的传动装置模式识别[J].自动化仪表,2008(06):4-8.
[11] 周峡,徐善顶.一种改进小波阈值函数的图像去噪方法研究[J].南京工程学院学报(自然科学版),2019,17(04):44-49.
[12] 王康,贺敬良,耿开贺,等.基于改进小波阈值-SVM的齿轮故障信号识别[J].机床与液压,2019,47(22):174-177.
[13] 刘自然,胡毅伟,石璞,等.基于改进经验小波变换的滚动轴承故障特征提取方法研究[J].中国测试,2019,45(10):10-15.

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备注/Memo

备注/Memo:
收稿日期：2020-06-06

作者简介：
韩芝星（1987），女，山西介休人，讲师/硕士研究生，研究方向为智能仪表与自动化装置方面。
更新日期/Last Update: 1900-01-01